A Comprehensive Analysis of Authorship in Radiology Journals
Bibliographic record
Abstract
OBJECTIVES: The purpose of our study was to investigate authorship trends in radiology journals, and whether International Committee of Medical Journal Editors (ICMJE) recommendations have had an impact on these trends. A secondary objective was to explore other variables associated with authorship trends. METHODS: A retrospective, bibliometric analysis of 49 clinical radiology journals published from 1946-2013 was conducted. The following data was exported from MEDLINE (1946 to May 2014) for each article: authors' full name, year of publication, primary author institution information, language of publication and publication type. Microsoft Excel Visual Basics for Applications scripts were programmed to categorize extracted data. Statistical analysis was performed to determine the overall mean number of authors per article over time, impact of ICMJE guidelines, authorship frequency per journal, country of origin, article type and language of publication. RESULTS: 216,271 articles from 1946-2013 were included. A univariate analysis of the mean authorship frequency per year of all articles yielded a linear relationship between time and authorship frequency. The mean number of authors per article in 1946 (1.42) was found to have increased consistently by 0.07 authors/ article per year (R² = 0.9728, P<0.001) to 5.79 authors/article in 2013. ICMJE guideline dissemination did not have an impact on this rise in authorship frequency. There was considerable variability in mean authors per article and change over time between journals, country of origin, language of publication and article type. CONCLUSION: Overall authorship for 49 radiology journals across 68 years has increased markedly with no demonstrated impact from ICMJE guidelines. A higher number of authors per article was seen in articles from: higher impact journals, European and Asian countries, original research type, and those journals who explicitly endorse the ICMJE guidelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchBibliometrics Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchBibliometricsResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.040 | 0.053 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".